from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.107	0.0	-1	1	NaN	0.048	0.002	NaN	0.235	0.235	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.071	0.0	-1	5	NaN	0.048	0.000	NaN	0.236	0.236	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.099	0.0	1	100	NaN	0.047	0.000	NaN	0.238	0.238	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.069	0.0	-1	100	NaN	0.048	0.000	NaN	0.235	0.235	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	6.998	0.0	1	5	NaN	0.047	0.000	NaN	0.243	0.243	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.152	0.0	1	1	NaN	0.048	0.000	NaN	0.234	0.234	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.375	0.0	-1	1	NaN	0.009	0.000	NaN	0.491	0.491	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.374	0.0	-1	5	NaN	0.009	0.000	NaN	0.489	0.489	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	NaN	0.355	0.0	1	100	NaN	0.009	0.000	NaN	0.518	0.518	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.360	0.0	-1	100	NaN	0.009	0.000	NaN	0.514	0.514	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	NaN	0.344	0.0	1	5	NaN	0.009	0.000	NaN	0.534	0.534	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.362	0.0	1	1	NaN	0.009	0.000	NaN	0.508	0.508	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.943	0.146	NaN	0.000	0.002	-1	1	0.663	0.183	0.006	0.687	10.634	10.640	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.003	NaN	0.000	0.022	-1	1	1.000	0.009	0.000	1.000	2.437	2.437	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.772	0.033	NaN	0.000	0.003	-1	5	0.757	0.182	0.002	0.742	15.251	15.252	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	NaN	0.000	0.023	-1	5	1.000	0.009	0.000	1.000	2.552	2.552	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.036	0.004	NaN	0.000	0.002	1	100	0.882	0.220	0.002	0.875	9.262	9.263	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	NaN	0.000	0.020	1	100	1.000	0.009	0.000	0.000	2.077	2.077	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.832	0.032	NaN	0.000	0.003	-1	100	0.882	0.226	0.012	0.875	12.537	12.556	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	NaN	0.000	0.024	-1	100	1.000	0.010	0.001	0.000	2.514	2.524	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.027	0.006	NaN	0.000	0.002	1	5	0.757	0.185	0.002	0.742	10.969	10.970	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	NaN	0.000	0.020	1	5	1.000	0.010	0.000	1.000	2.053	2.053	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.166	0.010	NaN	0.001	0.001	1	1	0.663	0.181	0.002	0.687	6.435	6.435	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	NaN	0.000	0.019	1	1	1.000	0.009	0.000	1.000	2.084	2.084	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.792	0.022	NaN	0.000	0.002	-1	1	0.896	0.028	0.000	0.967	65.055	65.061	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	NaN	0.000	0.006	-1	1	1.000	0.001	0.000	1.000	8.704	8.853	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.918	0.079	NaN	0.000	0.003	-1	5	0.922	0.028	0.000	0.974	103.529	103.543	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	NaN	0.000	0.006	-1	5	1.000	0.001	0.000	1.000	9.332	9.416	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.962	0.003	NaN	0.000	0.002	1	100	0.929	0.063	0.002	0.975	30.974	30.986	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	1.000	3.870	3.916	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.036	0.073	NaN	0.000	0.003	-1	100	0.929	0.064	0.001	0.975	47.291	47.296	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.003	NaN	0.000	0.010	-1	100	1.000	0.001	0.000	1.000	12.577	12.712	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.951	0.002	NaN	0.000	0.002	1	5	0.922	0.028	0.000	0.974	69.884	69.889	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	1.000	4.328	4.384	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.076	0.003	NaN	0.000	0.001	1	1	0.896	0.027	0.000	0.967	40.083	40.088	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	1.000	2.569	2.605	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.780	0.017	NaN	0.021	0.0	-1	1	NaN	0.790	0.069	NaN	4.783	4.802	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.420	0.043	NaN	0.018	0.0	-1	5	NaN	0.778	0.012	NaN	5.683	5.683	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.479	0.059	NaN	0.018	0.0	1	100	NaN	0.758	0.011	NaN	5.912	5.912	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.193	0.119	NaN	0.019	0.0	-1	100	NaN	0.780	0.008	NaN	5.379	5.379	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.923	0.068	NaN	0.020	0.0	1	5	NaN	0.768	0.015	NaN	5.108	5.109	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.046	0.054	NaN	0.020	0.0	1	1	NaN	0.775	0.011	NaN	5.223	5.224	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.021	0.0	-1	1	NaN	0.003	0.002	NaN	0.238	0.280	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	5	NaN	0.002	0.001	NaN	0.260	0.324	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	100	NaN	0.001	0.001	NaN	0.509	0.570	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	100	NaN	0.001	0.000	NaN	0.564	0.565	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	5	NaN	0.001	0.000	NaN	0.574	0.575	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	1	NaN	0.001	0.000	NaN	0.612	0.612	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.864	1.007	NaN	0.000	0.001	-1	1	0.929	0.124	0.003	0.910	6.960	6.962	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	1.000	11.931	12.817	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.128	0.339	NaN	0.000	0.001	-1	5	0.946	0.229	0.004	0.941	4.935	4.935	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	1.000	7.868	8.266	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.869	0.340	NaN	0.000	0.006	1	100	0.951	0.661	0.004	0.940	8.884	8.884	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	1.000	4.041	4.256	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.084	0.181	NaN	0.000	0.003	-1	100	0.951	0.662	0.006	0.940	4.662	4.662	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	0.001	0.000	1.000	6.389	6.750	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.792	0.289	NaN	0.000	0.002	1	5	0.946	0.241	0.005	0.941	7.429	7.430	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	0.000	0.000	1.000	3.572	3.895	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.969	0.261	NaN	0.000	0.001	1	1	0.929	0.121	0.001	0.910	8.017	8.018	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	3.675	3.960	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.010	NaN	0.001	0.000	-1	1	0.891	0.000	0.000	0.879	65.506	66.056	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.000	0.000	1.000	24.071	25.021	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	0.905	38.706	38.747	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	1.000	24.766	26.480	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.008	NaN	0.000	0.000	1	100	0.894	0.005	0.000	0.917	8.019	8.020	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	1.000	6.136	6.566	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.005	NaN	0.000	0.000	-1	100	0.894	0.005	0.002	0.917	7.376	7.947	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	1.000	21.885	22.958	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	NaN	0.001	0.000	1	5	0.911	0.001	0.000	0.905	31.682	31.724	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	1.000	6.807	7.149	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	NaN	0.001	0.000	1	1	0.891	0.000	0.000	0.879	49.441	49.892	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	6.436	6.774	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.538	0.108	30	0.030	0.0	random	NaN	0.390	0.027	NaN	1.380	1.383	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.583	0.038	30	0.027	0.0	k-means++	NaN	0.414	0.026	NaN	1.408	1.411	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.759	0.204	30	0.139	0.0	random	NaN	2.730	0.028	NaN	2.109	2.109	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.912	0.028	30	0.135	0.0	k-means++	NaN	3.147	0.087	NaN	1.878	1.879	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	random	0.001	0.0	0.0	0.001	9.283	15.508	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	1.000	0.0	0.0	1.000	8.623	13.784	See	See
4	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	k-means++	0.001	0.0	0.0	0.001	11.144	13.113	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	1.000	0.0	0.0	1.000	14.749	15.334	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.478	0.000	random	0.002	0.0	0.0	0.002	7.074	7.773	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	1.000	0.0	0.0	1.000	13.847	14.300	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.451	0.000	k-means++	0.002	0.0	0.0	0.001	7.571	8.218	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	1.000	0.0	0.0	1.000	13.085	13.335	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.075	0.001	20	0.002	0.0	random	NaN	0.029	0.004	NaN	2.644	2.670	See	See
3	KMeans_short	fit	10000	10000	2	0.211	0.002	20	0.001	0.0	k-means++	NaN	0.081	0.000	NaN	2.587	2.587	See	See
6	KMeans_short	fit	10000	10000	100	0.211	0.006	20	0.038	0.0	random	NaN	0.111	0.002	NaN	1.900	1.900	See	See
9	KMeans_short	fit	10000	10000	100	0.576	0.008	20	0.014	0.0	k-means++	NaN	0.319	0.003	NaN	1.805	1.805	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.000	0.0	-0.001	4.028	4.050	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	1.000	0.000	0.0	1.000	13.801	14.180	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.001	0.000	0.0	-0.001	3.760	3.790	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	1.000	0.000	0.0	1.000	12.520	12.955	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.308	0.000	random	0.279	0.001	0.0	0.294	2.725	2.736	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	1.000	0.000	0.0	1.000	11.343	11.512	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.313	0.000	k-means++	0.317	0.001	0.0	0.257	2.583	2.596	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	1.000	0.000	0.0	1.000	10.890	11.250	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.108	0.481	[20]	0.072	0.000	NaN	NaN	NaN	NaN	NaN	2.051	0.036	NaN	5.415	5.416	See	See
3	LogisticRegression	fit	1000	1000	10000	0.943	0.506	[26]	0.085	0.001	NaN	NaN	NaN	NaN	NaN	1.016	0.029	NaN	0.928	0.928	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.001	[20]	1.950	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.55	0.679	1.283	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.017	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	1.00	0.349	0.357	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	5.114	0.0	NaN	NaN	NaN	NaN	0.35	0.005	0.001	0.28	0.343	0.359	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.870	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.00	0.128	0.140	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.176	0.003	NaN	0.454	0.0	NaN	NaN	NaN	0.181	0.002	NaN	0.972	0.973	See	See
3	Ridge	fit	1000000	1000000	100	1.140	0.082	NaN	0.702	0.0	NaN	NaN	NaN	0.308	0.250	NaN	3.700	4.769	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	NaN	6.548	0.0	NaN	NaN	0.083	0.019	0.0	0.122	0.627	0.627	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	NaN	1.448	0.0	NaN	NaN	NaN	0.000	0.0	NaN	0.586	0.638	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	NaN	6.034	0.0	NaN	NaN	1.000	0.000	0.0	1.000	0.448	0.694	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	NaN	0.018	0.0	NaN	NaN	NaN	0.000	0.0	NaN	0.573	0.603	See	See